1. Field of the Invention
The present invention relates generally to the field of human health monitoring, and more particularly to the use of multivariate models for analysis of measurements of biological parameters to provide residual-based assessment of human health indicators.
2. Brief Description of the Related Art
Detection and diagnosis of disease conditions in humans is critically important to maintaining health of individuals and aiding in the recuperation of patients. While early and aggressive detection are highly desirable, this must be balanced with the health care delivery costs of false alerts and misdiagnoses. Accurate and actionable detection of unfolding health issues, whether in an time-critical setting such as an intensive care unit (ICU) or in longer-term health monitoring such as home monitoring of chronically ill people or performance monitoring of athletes for example, is a problem that has been the focus of much medical attention. Current demographic trends indicate that as people live longer, they have an increasing number of chronic health issues to deal with. In addition, some diseases that previously had high mortality are becoming manageable long-term chronic conditions. Consequently, an increasing number of people require ongoing monitoring, heavily taxing the healthcare delivery system.
According to conventional medical practice, human health monitoring in a critical care environment is typically practiced by means of a variety of real-time sensor measurements such as electrocardiogram (ECG), pulse oximetry, respiration and blood pressure, as well as laboratory tests of the blood, urine, and other bodily fluids. Longer term monitoring may include these variables, as well as weight measurement, medication dosing measurements and other qualitative assessments of condition. These measurements are typically compared in a univariate manner to prescribed normal ranges known for the (healthy) population generally, and indications of disease or developing conditions of poor health are diagnosed from these measurements vis-à-vis the standard ranges. Combined with qualitative observation by medically trained personnel, this forms the baseline practice in medicine in the monitoring of patients and healthy individuals for purposes of detecting ill health conditions and disease, and is a staff-intensive approach to the delivery of healthcare.
It has long been a goal of medical care to provide automated reliable monitoring of patients using sensors and computer technology. More recently, with the improved availability of digital instrumentation, and research into computer systems that embody the expertise of medical personnel, systems have been made available that provide for rules-based monitoring of patients based on vital signs and laboratory test results. Such systems are used in hospital settings to provide an auxiliary support system for monitoring patients in, e.g., an ICU. An expert rules execution engine can be programmed to combine threshold detection triggers across a variety of signals to diagnose or rule out a condition that requires human medical staff intervention.
These systems have met with limited success. The expert rules are difficult to design in a way that can be effectively generalized across the human population, and across the variety of states the patients present with. What may be accurate for a young trauma patient in recovery may be subject to false alerts and detection inaccuracies when applied to an elderly cardiology patient.
In another approach of the prior art, artificial intelligence techniques arguably more akin to the data fusion capabilities of the human expert, such as neural networks, have been applied to data from human patient monitoring in an attempt to provide better automated monitoring and diagnostics. A neural network is trained from a set of examples to learn certain associations and patterns. For example, a set of patient data associated with a disease state and another set of patient data associated with a healthy state are used to train the neural network to recognize the disease state and diagnose it. Typically, it has been known to input a set of patient data to a trained neural network and obtain a classification as output, either as a determination of health versus illness, or as a diagnosis of a particular condition. An alternative approach also known in the art is to input a set of patient data to a neural network and obtain a scalar rating value as output, e.g., degree of illness or progression of disease. However, the manner in which the neural network output was generated based on the input data and the training data is obscured to an observer because of the nonlinear nature of neural computing. Furthermore, it is difficult to design such a “black box” approach in a way that generalizes well beyond the training data. As a result, these approaches have met with wide variation in success, which ultimately undermines their reliability.
A major problem for all these prior art approaches is the dynamic nature of biological systems. Humans represent a biological system with a complex internal control and feedback system responsive to conditions and demands on the body for regulating critical aspects of health such as blood pressure, blood chemistry, oxygenation and the like. The measurements typically made to monitor health are subject to wide variation depending on activity state, age, weight, nutrition and disease state. As a consequence, it is difficult to assign proper trigger levels to thresholds for monitored variables and so these tend to be set on a demographic basis at levels that can only indicate critical and immediate health problems. For example, pulse rate monitoring may be set such that only extremely high or extremely low (or zero) pulse rates trigger an alarm. Even in more advanced prior art solutions combining variables and thresholds into multivariate rules, it remains extremely difficult to design rules that provide actionable lead time notice of a genuine developing problem while maintaining a low false alert rate. Similarly, neural networks for classification have been confounded with regard to generalizing because of the variation in the raw data. Needless to say, conventional statistical/demographic approaches in medicine cannot tolerate dynamic variation in the data, and either the data is acquired at extremely exacting conditions (e.g., the proper standardized conditions for a blood pressure test), or the variation is simply ignored, with concomitant loss in accuracy.
In the context of providing computer automated assistance in medical health monitoring, there is a significant need for improved approaches to processing and analyzing sensor and labs data from monitored humans, to provide accurate, actionable and early detection and diagnosis of incipient health problems. More particularly, what is needed is a system for leveraging existing sensor measurements to provide better computer automated vigilance of human health problems and to accurately prioritize which patients require the attention of human medical expertise. Improved automated monitoring would provide tremendous benefit in leveraging limited expert medical staff and improving overall healthcare delivery quality and efficiency.